
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase    :   Any						=			logging.get_logger(__name__)

lowercase    :   List[str]						=			{
    'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	_A								=	'timesformer'







	def __init__(   self						:List[Any]	,  a						:Tuple=2_2_4	,  a						:Optional[Any]=1_6	,  a						:List[str]=3	,  a						:Tuple=8	,  a						:Union[str, Any]=7_6_8	,  a						:Optional[int]=1_2	,  a						:List[str]=1_2	,  a						:Tuple=3_0_7_2	,  a						:Dict="gelu"	,  a						:List[Any]=0.0	,  a						:List[str]=0.0	,  a						:List[Any]=0.02	,  a						:int=1E-6	,  a						:Tuple=True	,  a						:List[Any]="divided_space_time"	,  a						:Union[str, Any]=0	,  **a						:Optional[int]	,  )						->					List[Any]:
								super().__init__(**a		)

								__UpperCamelCase      :  int       =   image_size
								__UpperCamelCase      :  List[str]       =   patch_size
								__UpperCamelCase      :  Any       =   num_channels
								__UpperCamelCase      :  List[Any]       =   num_frames

								__UpperCamelCase      :  Union[str, Any]       =   hidden_size
								__UpperCamelCase      :  str       =   num_hidden_layers
								__UpperCamelCase      :  Optional[Any]       =   num_attention_heads
								__UpperCamelCase      :  Union[str, Any]       =   intermediate_size
								__UpperCamelCase      :  Tuple       =   hidden_act
								__UpperCamelCase      :  List[str]       =   hidden_dropout_prob
								__UpperCamelCase      :  Any       =   attention_probs_dropout_prob
								__UpperCamelCase      :  Any       =   initializer_range
								__UpperCamelCase      :  Optional[int]       =   layer_norm_eps
								__UpperCamelCase      :  Dict       =   qkv_bias

								__UpperCamelCase      :  Tuple       =   attention_type
								__UpperCamelCase      :  Dict       =   drop_path_rate

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase    :   int						=			{
    'configuration_conditional_detr': [
        'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConditionalDetrConfig',
        'ConditionalDetrOnnxConfig',
    ]
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase    :   List[str]						=			['ConditionalDetrFeatureExtractor']
		lowercase    :   int						=			['ConditionalDetrImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase    :   Optional[int]						=			[
		    'CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ConditionalDetrForObjectDetection',
		    'ConditionalDetrForSegmentation',
		    'ConditionalDetrModel',
		    'ConditionalDetrPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_conditional_detr import (
		    CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    ConditionalDetrConfig,
		    ConditionalDetrOnnxConfig,
		)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
				from .image_processing_conditional_detr import ConditionalDetrImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_conditional_detr import (
				    CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ConditionalDetrForObjectDetection,
				    ConditionalDetrForSegmentation,
				    ConditionalDetrModel,
				    ConditionalDetrPreTrainedModel,
				)

else:
		import sys

		lowercase    :   Optional[int]						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)